Abstract
Simulation models are important tools for managing
water resources systems. An optimisation method coupled
with a simulation model can be used to identify
effective decisions to efficiently manage a system. The
value of a model in decision-making is degraded when
that model is not able to accurately predict system
response for new management decisions. Typically,
calibration is used to improve the predictability of
models to match more closely the system observations.
Calibration is limited as it can only correct parameter
error in a model. Models may also contain structural
errors that arise from mis-specification of model
equations. This research develops and presents a new
model error correction procedure (MECP) to improve the
predictive capabilities of a simulation model. MECP is
able to simultaneously correct parameter error and
structural error through the identification of suitable
parameter values and a function to correct
misspecifications in model equations. An evolutionary
computation (EC)-based implementation of MECP builds
upon and extends existing evolutionary algorithms to
simultaneously conduct numeric and symbolic searches
for the parameter values and the function,
respectively. Non-uniqueness is an inherent issue in
such system identification problems. One approach for
addressing non-uniqueness is through the generation of
a set of alternative solutions. EC-based techniques to
generate alternative solutions for numeric and symbolic
search problems are not readily available. New EC-based
methods to generate alternatives for numeric and
symbolic search problems are developed and investigated
in this research. The alternatives generation
procedures are then coupled with the model error
correction procedure to improve the predictive
capability of simulation models and to address the
non-uniqueness issue. The methods developed in this
research are tested and demonstrated for an array of
illustrative applications.
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